Testing GenAI Applications: Patterns That Actually Work

Everyone's shipping AI features. Nobody's talking about how to test them properly.

When AI becomes part of your application stack, traditional testing approaches break down fast. Non-deterministic outputs wreak havoc on CI pipelines, token costs spiral out of control, and teams struggle to build confidence in systems that make decisions they didn't explicitly program. Let's dive into test practices used in Envoy AI Gateway and Codename Goose. You'll see recorded response, local model serving, and LLM evaluation/benchmark techniques in non-theoretical applications.

This isn't about testing AI models themselves, but about maintaining software quality when intelligence becomes infrastructure. Whether you're integrating with external LLM APIs or running models locally, you'll walk away with concrete strategies for testing AI-powered applications that teams can implement immediately.